{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "8ed5a4d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>公司</th>\n",
       "      <th>职位</th>\n",
       "      <th>学历</th>\n",
       "      <th>薪资是否超过100k</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Google</td>\n",
       "      <td>销售主管</td>\n",
       "      <td>学士</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Google</td>\n",
       "      <td>销售主管</td>\n",
       "      <td>硕士</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Google</td>\n",
       "      <td>业务经理</td>\n",
       "      <td>学士</td>\n",
       "      <td>是</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Google</td>\n",
       "      <td>业务经理</td>\n",
       "      <td>硕士</td>\n",
       "      <td>是</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Google</td>\n",
       "      <td>计算机程序员</td>\n",
       "      <td>学士</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       公司      职位  学历 薪资是否超过100k\n",
       "0  Google    销售主管  学士          否\n",
       "1  Google    销售主管  硕士          否\n",
       "2  Google    业务经理  学士          是\n",
       "3  Google    业务经理  硕士          是\n",
       "4  Google  计算机程序员  学士          否"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "ori_data = pd.read_csv('data/薪资.csv', encoding='utf-8')\n",
    "ori_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "cd20f4ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>公司</th>\n",
       "      <th>职位</th>\n",
       "      <th>学历</th>\n",
       "      <th>薪资是否超过100k</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   公司  职位  学历  薪资是否超过100k\n",
       "0   2   2   0           0\n",
       "1   2   2   1           0\n",
       "2   2   0   0           1\n",
       "3   2   0   1           1\n",
       "4   2   1   0           0"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 导入sklearn的预处理子模块\n",
    "from sklearn import preprocessing\n",
    "\n",
    "# 标签编码器，用于转换分类数据\n",
    "label_encoder = preprocessing.LabelEncoder()\n",
    "# 将每列的分类数据转成数字，才能传递给模型\n",
    "data =pd.DataFrame()\n",
    "data['公司']= label_encoder.fit_transform(ori_data['公司'])\n",
    "data['职位']= label_encoder.fit_transform(ori_data['职位'])\n",
    "data['学历']= label_encoder.fit_transform(ori_data['学历'])\n",
    "data['薪资是否超过100k'] = label_encoder.fit_transform(ori_data['薪资是否超过100k'])\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "2308290f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>公司</th>\n",
       "      <th>职位</th>\n",
       "      <th>学历</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   公司  职位  学历\n",
       "0   2   2   0\n",
       "1   2   2   1\n",
       "2   2   0   0\n",
       "3   2   0   1\n",
       "4   2   1   0"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 将数据集的特征X和标签Y分开\n",
    "feature_cols = ['公司','职位','学历']\n",
    "tartget_names = label_encoder.classes_\n",
    "X = data[feature_cols]\n",
    "y = data['薪资是否超过100k']\n",
    "X[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "4c63f4df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    0\n",
       "2    1\n",
       "3    1\n",
       "4    0\n",
       "Name: 薪资是否超过100k, dtype: int32"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "c3ab59a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier # 导入决策树模型\n",
    "from sklearn.model_selection import train_test_split # 导入训练集测试集切分函数\n",
    "from sklearn import metrics # 导入sklearn的模型评价子模块"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "b09580a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test,y_train,y_test = train_test_split(X,y,test_size=0.3,random_state=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "a54186a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>公司</th>\n",
       "      <th>职位</th>\n",
       "      <th>学历</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    公司  职位  学历\n",
       "6    0   2   1\n",
       "14   1   1   0\n",
       "5    2   1   1\n",
       "2    2   0   0\n",
       "4    2   1   0"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "27908c92",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.4\n"
     ]
    }
   ],
   "source": [
    "# 创建决策树分类器，使用entropy作为分裂准则\n",
    "clf_entropy = DecisionTreeClassifier(criterion=\"entropy\", max_depth=3, random_state=100)\n",
    "# 在训练集上进行训练\n",
    "clf_entropy = clf_entropy.fit(X_train,y_train)\n",
    "# 在测试集上进行预测\n",
    "y_pred = clf_entropy.predict(X_test)\n",
    "\n",
    "# 输出模型的准确率\n",
    "print(\"Accuracy:\", metrics.accuracy_score(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "21c9ae07",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.0\n"
     ]
    }
   ],
   "source": [
    "# # 创建决策树分类器，使用gini为分裂准则\n",
    "clf_gini = DecisionTreeClassifier(criterion=\"gini\",random_state=100,\n",
    "                                  max_depth=3, min_samples_leaf=5)\n",
    "# Performing training\n",
    "clf_gini.fit(X_train, y_train)\n",
    "y_pred = clf_gini.predict(X_test)\n",
    "\n",
    "print(\"Accuracy:\", metrics.accuracy_score(y_test,y_pred))\n",
    "\n",
    "# entropy 关注数据集的混乱程度，而 gini 关注数据集中不同类别样本的混合程度。\n",
    "# entropy 越大，数据集越混乱，分类难度越大；gini 越小，数据集越纯净，分类难度越小\n",
    "# 对于数据集较小的情况，可以使用 entropy，因为 entropy 计算更简单，更容易收敛。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "fa09e6c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import tree\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "matplotlib.rcParams['font.family'] ='Microsoft Yahei'\n",
    "tree.plot_tree(clf_entropy, filled=True, \n",
    "               feature_names=feature_cols, class_names=['否', '是'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "3b344a42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tree.plot_tree(clf_gini, filled=True, \n",
    "               feature_names=feature_cols, class_names=['否', '是'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "26a5ef83",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|--- 公司 <= 0.50\n",
      "|   |--- class: 0\n",
      "|--- 公司 >  0.50\n",
      "|   |--- 职位 <= 1.50\n",
      "|   |   |--- 职位 <= 0.50\n",
      "|   |   |   |--- class: 1\n",
      "|   |   |--- 职位 >  0.50\n",
      "|   |   |   |--- class: 1\n",
      "|   |--- 职位 >  1.50\n",
      "|   |   |--- class: 0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import export_text\n",
    "r_entropy = export_text(clf_entropy, feature_names=feature_cols)\n",
    "r_gini = export_text(clf_gini, feature_names=feature_cols)\n",
    "print(r_entropy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "a1c8ea77",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|--- 公司 <= 1.50\n",
      "|   |--- class: 0\n",
      "|--- 公司 >  1.50\n",
      "|   |--- class: 1\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(r_gini)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cc803539",
   "metadata": {},
   "outputs": [],
   "source": [
    "# !conda install anaconda::graphviz\n",
    "# !conda install python-graphviz\n",
    "\n",
    "# graphviz  安装graphviz软件，包含命令行工具\n",
    "#  python-graphviz  Python 中创建和可视化 Graphviz 图形"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "0dfe1f52",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "digraph Tree {\n",
      "node [shape=box, style=\"filled, rounded\", color=\"black\", fontname=\"Microsoft Yahei\"] ;\n",
      "edge [fontname=\"Microsoft Yahei\"] ;\n",
      "0 [label=<公司 &le; 0.5<br/>entropy = 0.994<br/>samples = 11<br/>value = [5, 6]<br/>class = 是>, fillcolor=\"#deeffb\"] ;\n",
      "1 [label=<entropy = 0.0<br/>samples = 3<br/>value = [3, 0]<br/>class = 否>, fillcolor=\"#e58139\"] ;\n",
      "0 -> 1 [labeldistance=2.5, labelangle=45, headlabel=\"True\"] ;\n",
      "2 [label=<职位 &le; 1.5<br/>entropy = 0.811<br/>samples = 8<br/>value = [2, 6]<br/>class = 是>, fillcolor=\"#7bbeee\"] ;\n",
      "0 -> 2 [labeldistance=2.5, labelangle=-45, headlabel=\"False\"] ;\n",
      "3 [label=<职位 &le; 0.5<br/>entropy = 0.592<br/>samples = 7<br/>value = [1, 6]<br/>class = 是>, fillcolor=\"#5aade9\"] ;\n",
      "2 -> 3 ;\n",
      "4 [label=<entropy = 0.0<br/>samples = 3<br/>value = [0, 3]<br/>class = 是>, fillcolor=\"#399de5\"] ;\n",
      "3 -> 4 ;\n",
      "5 [label=<entropy = 0.811<br/>samples = 4<br/>value = [1, 3]<br/>class = 是>, fillcolor=\"#7bbeee\"] ;\n",
      "3 -> 5 ;\n",
      "6 [label=<entropy = 0.0<br/>samples = 1<br/>value = [1, 0]<br/>class = 否>, fillcolor=\"#e58139\"] ;\n",
      "2 -> 6 ;\n",
      "}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'data\\\\salaries_entropy.pdf'"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import export_graphviz\n",
    "import graphviz\n",
    "dot_data = export_graphviz(clf_entropy, out_file=None,\n",
    "                           # feature_names=['company','job','degree'],\n",
    "                           feature_names=feature_cols,\n",
    "                           class_names=['否', '是'],\n",
    "                           filled=True, rounded=True,\n",
    "                           special_characters=True,\n",
    "                           fontname='Microsoft Yahei'\n",
    "                           )\n",
    "print(dot_data)\n",
    "graph = graphviz.Source(dot_data)\n",
    "graph.render(\"data/salaries_entropy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "ac491b39",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "digraph Tree {\n",
      "node [shape=box, style=\"filled, rounded\", color=\"black\", fontname=\"Microsoft Yahei\"] ;\n",
      "edge [fontname=\"Microsoft Yahei\"] ;\n",
      "0 [label=<公司 &le; 1.5<br/>gini = 0.496<br/>samples = 11<br/>value = [5, 6]<br/>class = 是>, fillcolor=\"#deeffb\"] ;\n",
      "1 [label=<gini = 0.5<br/>samples = 6<br/>value = [3, 3]<br/>class = 否>, fillcolor=\"#ffffff\"] ;\n",
      "0 -> 1 [labeldistance=2.5, labelangle=45, headlabel=\"True\"] ;\n",
      "2 [label=<gini = 0.48<br/>samples = 5<br/>value = [2, 3]<br/>class = 是>, fillcolor=\"#bddef6\"] ;\n",
      "0 -> 2 [labeldistance=2.5, labelangle=-45, headlabel=\"False\"] ;\n",
      "}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'data\\\\salaries_gini.pdf'"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dot_data = export_graphviz(clf_gini, out_file=None,\n",
    "                           # feature_names=['company','job','degree'],\n",
    "                           feature_names=feature_cols,\n",
    "                           class_names=['否', '是'],\n",
    "                           filled=True, rounded=True,\n",
    "                           special_characters=True,\n",
    "                           fontname='Microsoft Yahei'\n",
    "                           )\n",
    "print(dot_data)\n",
    "graph = graphviz.Source(dot_data)\n",
    "graph.render(\"data/salaries_gini\")"
   ]
  }
 ],
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